# 6. Machine Learning with Random Survival Forests

## Introduction

While classical models like the Cox Proportional Hazards model are highly interpretable, they rely on strict assumptions of linear, additive effects. Random Survival Forests (RSF) overcome this by building a non-linear ensemble of decision trees.

In this tutorial, we will demonstrate how to run a standalone RSF using `SuperSurv`’s unified wrappers, evaluate its individual performance, and finally include it in a Super Learner ensemble.

## 1. Prepare the Data

    library(SuperSurv)
    library(survival)

    data("metabric", package = "SuperSurv")
    set.seed(123)

    train_idx <- sample(1:nrow(metabric), 0.7 * nrow(metabric))
    train <- metabric[train_idx, ]
    test  <- metabric[-train_idx, ]

    X_tr <- train[, grep("^x", names(metabric))]
    X_te <- test[, grep("^x", names(metabric))]

    new.times <- seq(50, 200, by = 25)

## 2. Standalone Machine Learning

`SuperSurv` provides unified wrappers that automatically handle the training and standardization of survival probabilities. You can use these completely independent of the Super Learner ensemble.

    # 1. Fit the standalone wrapper
    rf_standalone <- surv.rfsrc(
      time = train$duration,
      event = train$event,
      X = X_tr,
      new.times = new.times
    )

    # 2. Extract the fitted model object and prediction matrix
    rf_fit <- rf_standalone$fit
    rf_pred_matrix <- rf_standalone$pred

Because our plotting functions are universally compatible, we can plot individual patient curves directly from this standalone matrix:

    # Plot the first 3 patients in our training set
    plot_predict(preds = rf_pred_matrix, eval_times = new.times, patient_idx = 1:3)

## 3. Evaluating the Standalone Model

We can also pass this standalone model directly into our evaluation suite to test its performance on new data.

    # The function automatically detects this is a single model and plots it!
    plot_benchmark(
      object = rf_fit,
      newdata = X_te,
      time = test$duration,
      event = test$event,
      eval_times = new.times
    )

    # plot_calibration(
    #   object   = rf_fit,
    #   newdata  = X_te,
    #   time     = test$duration,
    #   event    = test$event,
    #   eval_time = 150,
    #   bins     = 2
    # )

## 4. Train the Benchmark Ensemble

While the standalone RSF is powerful, we can objectively evaluate if it outperforms classical models by putting them together in a `SuperSurv` ensemble.

    my_library <- c("surv.coxph", "surv.weibull", "surv.rfsrc")

    fit_supersurv <- SuperSurv(
      time = train$duration,
      event = train$event,
      X = X_tr,
      newdata = X_te,
      new.times = new.times,
      event.library = my_library,
      cens.library = c("surv.coxph"),
      control = list(saveFitLibrary = TRUE),
      verbose = FALSE,
      nFolds = 3
    )

## 5. Visualizing Ensemble vs. Base Learners

When we pass the `SuperSurv` ensemble into the exact same benchmark function, it automatically unpacks the library and plots the ensemble against all its constituent models.

    plot_benchmark(
      object = fit_supersurv,
      newdata = X_te,
      time = test$duration,
      event = test$event,
      eval_times = new.times
    )

By incorporating advanced machine learning algorithms into your library, `SuperSurv` mathematically guarantees that your final predictions adapt to the complexity of your data, achieving the lowest possible Brier Score.